=Paper=
{{Paper
|id=Vol-2518/paper-WODHSA6
|storemode=property
|title=Modeling Concept Drift for Historical
Research in the Digital Humanities
|pdfUrl=https://ceur-ws.org/Vol-2518/paper-WODHSA6.pdf
|volume=Vol-2518
|authors=Claudio Masolo,Emilio M. Sanfilippo,Marion Lamé,Perrine Pittet
|dblpUrl=https://dblp.org/rec/conf/jowo/MasoloSLP19
}}
==Modeling Concept Drift for Historical
Research in the Digital Humanities==
https://ceur-ws.org/Vol-2518/paper-WODHSA6.pdf
Modeling Concept Drift for Historical
Research in the Digital Humanities
Claudio MASOLO a,1 , Emilio M. SANFILIPPO b,c , Marion LAMÉ b,c and
Perrine PITTET c
a ISTC-CNR Laboratory for Applied Ontology, Trento, Italy
b Le Studium Institute for Advanced Studies, Orléans and Tours, France
c CESR UMR 7323 – University of Tours, France
Abstract. In Digital Humanities one often needs to deal with the modeling of con-
cept drift, i.e., the mechanism underlying changes in concepts’ intensions. This
particularly applies to historical research, e.g., when experts study changes in lan-
guage or entire conceptual frameworks. The purpose of the paper is to address some
challenges concerning the ontological modeling of both concepts and concept drift.
We sketch an initial formal approach which allows to consider the temporal di-
mension of concept characterization and, consequently, temporal relations between
multiple concepts. This eventually leads us to discuss some preliminaries ideas on
the formal treatment of experts’ intentional attitudes towards the transmission and
manipulation of concepts inherited from the past.
Keywords. Concept, concept drift, history, history of ideas
1. Introduction
Domain experts working in the Digital Humanities, especially in research contexts re-
lated to history and history of ideas, often need to study the relations holding between
concepts traced at different periods in time [9]. For instance, a domain expert may be
interested in understanding how the concept of science adopted in ancient Greece re-
lates to the concept of science used by contemporary Western scientists.2 Simplifying
the research methodology for the sake of easiness, by investigating and studying multi-
ple sources, historians first try to define (or characterize) the concepts at stake and then
understand their links by taking into account multiple parameters (e.g., related to the cul-
tural bias of contemporary historians when approaching past societies) [7]. In this con-
text, an important research question concerns whether concepts can change in time while
preserving their identity or whether their characterizations are rather fixed.
In studies related to history of ideas, computer science, and philosophy, the phe-
nomenon of concept change is also called concept drift [1,3]. From a theoretical per-
1 Corresponding Author: Laboratory for Applied Ontology, via alla cascata 56/C, 38123, Trento, Italy; E-
mail: masolo@loa.istc.cnr.it. Copyright c 2019 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
2 Depending on the application domain, concepts analyzed by historians can be very broad including politics,
science, culture, agriculture, artefacts, etc.
spective, it is a matter of debate what concept drift amounts to, for instance, what are the
conditions that a concept needs to satisfy to keep its identity in time. From a computer
science perspective, a formal model of concept drift for historical modeling purposes is
still lacking. This model could be useful to help historians in their analysis of concepts,
but also to make accessible and comparable the data originating from historical research.
We present in the following a preliminary analysis of concept drift ultimately aimed
at the design of ontology-based systems for historical data. By reviewing previous works
on the history of ideas, we sketch a formal model of concept drift which stresses the
intentional nature of concepts and is able to take into account their originating contexts.
The remaining of the paper is structured as follows. Section 2 presents some general
aspects of concept theories. Given the plethora of work done in different studies, our
purpose is just to introduce some characteristics commonly ascribed to concepts. Section
3 clarifies the distinction between term drift and concept drift which is sometimes blurred
in the literature. Section 4 reports some discussions in the state of the art on the history
of ideas about concept drift. We consider this literature because it explicitly addresses
issues concerning the understanding and modeling of concept drift that are relevant from
a computer science perspective (e.g., how multiple versions of the same concept relate
to each other). Section 5 introduces some basic formal tools that we use to analyze the
state of the art and to frame our proposal. Section 6 adds some preliminary ideas for a
new approach to model concepts for historical research that relies on the way the authors
intentionally refer to pre-existing concepts. Section 7 concludes the paper.
2. Concepts
The nature of concepts is highly debated across cognitive sciences, philosophy, and lin-
guistics [10]. Independently from this heterogeneity, scholars agree that a core function-
ality of concepts is to classify (categorize) other entities. For example, the concept per-
son classifies human beings. It is also common to find the distinction between concepts’
extension and intension, where the former refers to the (set of) individuals classified by
a concept while the latter to its definition or characterization (that, in principle, allows to
individuate concepts’ instances). A concept’s intension is not reducible to its extension,
first, because the extension can change without affecting the intension (e.g., if Paul dies,
the definition of person does not change); second, because concepts with different inten-
sions (e.g., being an animal with heart and being an animal with kidney) can have the
same extensions.
Intensionally, concepts are usually seen as complex entities characterized in terms
of other concepts (see Sect. 5 for more details). The way concepts are characterized and
how such characterization is exploited in the classification (categorization) process cause
some disagreements among concepts theories. In the classical view, a concept is reduced
to a conjunction of necessary and sufficient conditions; accordingly, an individual is ei-
ther categorized or not under a concept, hence there is no uncertainty and all the in-
stances of a concept are equally treated. By contrast, categorization under everyday con-
cepts often presents typicality effects and a certain degree of indeterminacy. This pushed
cognitive scientists to develop alternative models to match empirical data (see [10] for
a review). For instance, in the prototype theory the categorization of an individual un-
der a concept depends on the degree of similarity between the individual and the proto-
type of the concept. The degree of similarity is calculated by considering (i) the salience
weights contextually assigned to the attributes of a given concept (e.g., for apples, shape
may be more salient than color); and (ii) the typicality weights contextually assigned to
attribute-values (e.g., red apples are more typical than brown apples).
The approaches in the field of the history of ideas only seldom endorse an explicit
theory of concepts. There is indeed the underlying assumption that concept drift can
be described without a strong commitment on the nature of concepts. The approaches
presented in [1] and [8], which we will further analyze in the paper, assume that concepts
are intentionally introduced at given times for specific aims and are characterized by
simpler concepts called features. In the following sections, we start by analyzing the
temporal and structural aspects of concepts and then see how these aspects can be used
to compare alternative positions on concept drift. Before that, an informal clarification
on concept and term drift is due.
3. Term and Concept Drift
For the sake of clarity, we distinguish between term drift and concept drift.
Term drift can be understood in different ways. A first case is when a term w1 (w
for word) used at time t1 with meaning m is replaced at t2 with a term w2 6= w1 which
however keeps m as meaning. This phenomena can be due to multiple factors, for in-
stance, linguistic changes that occur within the language used at different times by differ-
ent agents. A typical example is about morphological changes in the spelling of words,
e.g., the way in which toponyms change in time [5]. A second case of term drift occurs
when the same term w is used at different times but with different meanings, i.e., w has
meaning m1 at t1 and meaning m2 6= m1 at t2 . An example coming from the analysis of
cooking texts in the French tradition concerns the term ‘blanc manger’. During the Mid-
dle Age, this term had the meaning of a salty dish prepared with either fish or white meat
while nowadays it means a white pudding (a dessert) [9]. The first case is a syntactic
change of the term that is however used with the same meaning; the second case is a shift
in meaning—a semantic change in Geeraerts’ terms [6, ch.1]3 —likely due to the way in
which the same term happens to be differently used across history.
Theories of concept drift focus on analyzing the mechanisms underlying the way
in which concepts (possibly) change over time. A core issue is whether a concept keeps
its identity when its characterization, i.e., its intention, changes. This issue recalls in-
vestigations in formal ontology about the identity of (physical) objects when some of
their properties or components change through time [4]. A plethora of positions exist
and some philosophers argue that identity and persistence conditions for objects cannot
be set in a pure a priori way, but reference to domain knowledge is necessary [12]. For
instance, just as it is the task of mathematicians to establish the conditions for two sets
being identical or different, it is up to physicists to establish when two material bodies
traced at different times are numerically the same individual. The case of concepts drift,
considering the abstract and complex nature of concepts, is still more challenging. In
Betti and van den Berg’s words [1]: “The question of what concepts are and how they
retain their identity over time is fundamentally a philosophical one [...] and there is no
3 Geeraerts [6, ch.1] discusses and classifies several approaches to semantic change.
conclusive reason to take the belief that concepts retain their identity over time to be
true.”
From these considerations, it should be clear that term drift and concept drift cannot
be identified: the first phenomenon concerns changes in linguistic entities and/or their
meanings, while the second one is about concepts and their intensions, which do not
necessarily bear a linguistic nature.
4. Mapping the Debate on Concept Drift
The debate on concept drift can be mapped into two main segments. In the first approach,
concepts are intensionally static and invariable. For example, it is not possible for a
concept C to be characterized by the features a and b at time t, and by the features a
and c at time t 0 . Rather, we have two different concepts, namely, C1 existing at t and
characterized by a and b, and C2 6= C1 existing at t 0 and characterized by a and c. One
can however study the degree of similarity between (the characterizations of) C1 and C2 .
In the example, C1 and C2 share the feature a while being different concepts.
In the second approach, concepts can change (at some degree) their intensions while
retaining identities. Mink [11], for instance, commenting on Lovejoy’s view on the non-
changing nature of concepts, argues that “[i]t is not necessary to mount a massive attack
on Lovejoy’s claim that unit-ideas remain identical in all appearances; one needs only
put it under the lens of the historical sense for the anti-historical nature of this method-
ological belief to reveal itself.” In Mink’s view, therefore, dismissing the dynamic nature
of concepts amounts to dismissing the very nature of historical analysis. In the previous
example one can consider a single dynamic concept C that changes its characterization
from t to t 0 : C retains the feature a from t to t 0 but the feature b is substituted with c.
The dynamic approach is forced to specify the persistence conditions of concepts to
limit the way in which they can change. To deal with this issue and allow for restricted
concepts change, Kuukkanen [8] argues that when studying and comparing multiple con-
cepts for historical analysis purposes, it is necessary to distinguish between concepts’
core and marginal features. Core features are those that a concept must maintain during
its whole life, whereas marginal features can be lost or acquired. For instance, following
Kuukkanen, being material and being indecomposable are core feature of the concept of
element, whereas being found in all bodies or being the ultimate constituents of bodies
are marginal features (endorsed only by some authors).
On the basis of the core vs. marginal features dichotomy, Kuukkanen distinguishes
three possible temporal behaviors of concepts: (i) concept stability: a concept keeps
through time both its core and marginal features; (ii) concept change: a concept keeps
through time its core features but it undergoes changes in its marginal features; (iii) con-
cept replacement: the core of a concept definition changes in time. For instance, suppose
a concept C has only one core feature, say, a. When C has the features a and b at all times
at which it exists it is stable. When C is defined by a and b at time t, and by a and c at
t 0 we have a genuine concept change: C changes but only marginally while keeping its
core feature a. Vice versa, C defined by a and b at time t is replaced by C0 at t 0 defined
by c and d because it lost its core feature a.
From a general perspective, first, it is unclear how to establish that a concept re-
places another. E.g., in the case of two concepts C and C0 characterized by a and b, c
and d, respectively, it seems to be an intentional choice of the historian to consider C0
as replacing C, since there is nothing in the features suggesting the replacement, i.e.,
the model does not make explicit why C0 (and not, for example, C00 characterized by b
and c) replaces C. One possibility we consider in Sect.6 is to complement the feature-
based characterization of concepts with an intentional stance on concepts creation and
transmission mechanisms.
Second, the role of core and marginal features and the way they are identified is not
clear. Kuukkanen himself recognises that “there is no naturally carved line between the
central/essential versus marginal features” [8, p. 370-71]. We think that this distinction
can be left out without the loss of any expressive power. For instance, studying the con-
cept of science in Kant and Brentano, a historian may first identify two stable concepts,
SK (Kant’s view) and SB (Brentano’s view). Supposing that SK and SB share some fea-
tures, Kuukkanen would probably introduce a new dynamic concept S where the core
features are the ones shared by both SK and SB , whereas the marginal features are the ones
characterizing only SK and only SB . Alternatively, it is possible to introduce a more gen-
eral stable concept S0 characterized only by the features shared by both SK and SB . In this
case the links between S0 , SK , and SB are not lost, SK and SB are different specializations
of S0 . One can still compare concepts on the basis of their characterizations committing
neither to the core vs. marginal dichotomy, nor to the dynamicity of concepts.
Betti and van den Berg [1] seem to adopt this second approach. In addition, they
propose to characterize concepts within broader conceptual structures called models.
The idea, inherited from cognitive science, is that models provide context-dependent
networks of concepts whose inter-relations contribute to the definition of the concepts
themselves. In this framework, specialization between concepts does not reduce to set-
theoretical relations between their features, but it is instead based on more general links
between the models that characterize the concepts. It is then possible that two special-
izations of a concept do not share any feature. Unfortunately, similarly to Kuukkanen’s
work, this approach remains at a high abstraction level.
In the next section, we use some formal tools to better analyze and compare the ap-
proaches in [8] and [1]. Despite the high-level similarities, there are relevant differences
between these approaches which can be better grasped by formal means. More generally,
our analysis aims at shedding some light on concepts’ characterization and concept drift.
5. Formal Tools for Concept and Concept Drift Modeling
We start by analyzing the relation between a concept C and its intensional characteriza-
tion φ . For this purpose, we introduce a partial function γ that associates to a concept C,
at any time (or context) t at which it exists, its characterization φ . Concepts are assumed
to be in time (or to exist in contexts) and, usually, to have a bounded life.
Before discussing the possibility for a concept to undergo a change in its charac-
terization (Sect. 5.3), hence the possibility to have γ(C,t) = φ and γ(C,t 0 ) = φ 0 with
φ 6= φ 0 , we clarify the nature of both the characterization φ (Sect. 5.1) and the function γ
(Sect. 5.2). Finally, following [1], Sect. 5.4 introduces models, i.e., networks of concepts,
to define (and compare) concepts in relational and structural terms.
5.1. Characterizations
In Sect. 2, we saw that concepts are commonly defined, or simply characterized, in terms
of simpler concepts called features. In a non relational perspective, characterizations can
be simply modeled as (finite) sets of features, i.e., φ = {F1 , . . . , Fn }, that can be easily
linked by means of set-theoretic relations.
To represent the distinction introduced by Kuukkanen [8] between core and margin
features, each characterization φ can be partitioned into a set φ c of core features and a
set φ m of margin features, i.e., φ = φ c ∪ φ m and φ c ∩ φ m = 0.
/ Characterizations can then
be linked by considering the set-theoretical relations between the core-subsets and the
margin-subsets separately.
Betti and van den Berg [1] refer also to the determinate-determinable distinction
[13]. More precisely, they discuss the possibility to taxonomically organize features by
means of an (intensional) subsumption relation, here noted vft : F vft F 0 stands for “the
feature F is subsumed by, specializes, the feature F 0 .” Subsumption allows to express
new links between the characterizations. For instance, the set-theoretical inclusion φ ⊆ φ 0
between characterizations, stating that φ 0 is more specific than φ because it contains
more features, can be generalized by considering vft : φ 0 v φ (note the different order
of φ and φ 0 ) if and only if for all features F ∈ φ there exists a feature F 0 ∈ φ 0 such that
F 0 vft F, i.e., all the features in φ are specialized by a feature in φ 0 . Given the fact that
F vft F, when φ ⊆ φ 0 , for each F ∈ φ it is enough to consider F 0 = F to see that φ ⊆ φ 0
is just a special case of φ 0 v φ .
As mentioned in Sect. 2, cognitive approaches to concepts consider additional in-
formation about features (usually called attribute-values): (i) features are partitioned by
attributes or kinds; (ii) features are structured by means of geometric or topological re-
lations; (iii) features and attributes are weighted, respectively, in terms of their typical-
ity and salience in the context of a given concept; (iv) subsumption (defined between
features of the same kind) is usually distinguished from correlation (defined between
features in different kinds).4
Depending on the information one disposes of, it is possible to establish relations
R(φ , φ 0 ) between characterizations with different levels of precision and granularity. As
we will see in the next sections, by relying on richly structured features, subtle analyses
about the (dis-)similarity of charactizations and concepts are possible.
5.2. The Function γ
The function γ individuates how a concept is characterized at a given time or context,
i.e., considering the previous discussion, what are the features associated to it.
Betti and van den Berg [1] claim that features are components of concepts, i.e., there
exists a parthood relation defined between features and concepts. The function γ can
be hence intended to collect all the components that C has at t, i.e., F ∈ γ(C,t) if and
only if Part(F,C,t). In this view, concepts are complex entities composed by features
4 Cognitive approaches are commonly interested in determining the similarity between objects, or the degree
of classification of an object under a concept characterized by, e.g., a given prototype, while they do not usually
consider similarity between prototypes.
(i.e., they are mereological sums of features, see [12] for details on mereologies).5 Still it
remains unclear whether features have to be intended as properties of the instances of the
concepts C they characterize, i.e., whether F ∈ γ(C,t) and C(x,t) imply F(x,t), where
C(x,t) (F(x,t)) stands for “at t, the entity x is an instance of the concept C (the feature
F)”. Kukkanen [8] talks of features as components, too, but he seems to accept among
features also properties of concepts so that it is possible to have F ∈ γ(C,t) and F(C,t),
i.e., at t, the concept C is an instance of the feature F, C has the property F.
Both [1] and [8] agree on ascribing to features and concepts the same nature; fea-
tures are just simpler concepts. However, there is no further discussion about what kinds
of properties are included among the features. This makes also unclear whether the fea-
tures characterizing a concept C completely determine C or it is rather possible to have
different concepts with exactly the same features.6
Given these remarks, we commit only to a weak notion of characterization where
features only partially characterize what concepts are, i.e., concepts do not reduce to
sets of features (Sect. 6 further discusses this view). Furthermore, we do not assume that
features are enough to determine the extension of concepts. Even though [1,8] do not
dedicate much attention to the classificatory role of concepts (not indeed fundamental
for the remaining of our discussion), this aspect is crucial for cognitive theories, which
usually rely on elaborated classification rules (grounded on features together with their
taxonomical organization, salience weights, etc.). Note however that the intension of a
concept is usually intended to influence, if not determine, its extension, i.e., the extension
depends on the intension. By avoiding to specify the way the features of a concept are
used to classify individuals we loose this link.
5.3. Analyzing Relations Between Concepts: the Case of Concept Drift
Let us consider again the concept of science discussed by Betti and van den Berg [1],
which we take as a useful example throughout this section to discuss about concept drift.
Presumably, as scholars interested in the history of ideas, they started from a detailed
analysis of Kant’s and Brentano’s positions about science. The first analytical step con-
sists therefore in characterizing the two positions as precisely as possible; let us refer
to them by SK and SB , respectively.7 Before studying how SK and SB are inter-linked,
e.g., if they are the same concept or not, the historian must consider how SK and SB are
characterized by their authors and, in particular, what are their features. Suppose that
SK exists at least at context kant with γ(SK , kant) = φK and SB exists at least at context
brentano with γ(SB , brentano) = φB . The case where φK 6= φB makes evident that scholars
concluded that the concepts of science of Kant and Brentano have different peculiarities
even though they can be potentially assimilated into a general concept of science.
5 We intend features as mereologically atomic, i.e., simple concepts without components such that γ(F,t) =
{F} holds during the whole life of any feature F. If features are complex concepts, in their turn, they have
simpler features as components, i.e., it is possible to have Part(F,C,t) and Part(F 0 , F,t) with F 0 6= F. Usually
Part is considered as transitive, therefore it follows that Part(F 0 ,C,t), i.e., F 0 is also a feature of C. At this point,
unless infinitely decomposable concepts exist, it is enough to reiterate the decomposition process until reaching
undecomposable concepts and include only them among the features that are used in the characterizations φ .
6 By assuming an extensional mereology [12] to model parthood relations between concepts and their fea-
tures (as components), the possibility of having different concepts with the same features would be excluded.
7 To simplify our analysis, we assume that Kant and Brentano did not change their positions about science.
In presence of changes, the analysis needs to consider all these positions.
The second analytical step aims at understanding the degree of similarity between
the characterizations φK and φB . An interesting case is when φK and φB share some com-
mon features (φK ∩ φB 6= 0) / but no characterization is included in the other (φK * φB and
φB * φK ). When the features in φK ∩ φB are relevant, Kuukkanen would probably commit
to the existence of a general dynamic concept S of science with core features included in
φK ∩ φB and marginal features including (φK \φB )∪(φB \φK ). S, such that γ(S, kant) = φK
and γ(S, brentano) = φB , would persist through contexts kant and bolzano maintaining its
core features but changing the marginal ones. Vice versa, Betti and van den Berg would
probably introduce a third stable concept S characterized only by φ = φK ∩ φB , i.e., such
that both φK and φB are specializations of φ (φ ⊆ φK and φ ⊆ φB trivially hold).
In the first analysis it is not clear whether there exists only the dynamic (and general)
concept S that changes from Kant to Bolzano or, in addition to S, one needs to consider
also the stable concepts of science of Kant and Bolzano. The second analysis clearly
commits to three different (stable) concepts: SK , SB , and S. The link between SK and
SB is here established through the intermediation of S that generalizes both SK and SB .
By relying on the general specialization relation v (see Sect. 5.1), this indirect link can
be introduced even when φK ∩ φB = 0/ (where the approach based on core-features is not
applicable).8
The individuation of the persistence conditions of S or its level of generality is usu-
ally a matter of historical investigation, it is not intrinsically determinated by the char-
acterizations of SK and SB . There is a gap between the concepts SK and SB on the one
hand, and the concept S on the other hand, that may be introduced by historians for an-
alytic purposes. This gap raises the problem of establishing the temporal or contextual
extension of concepts. For instance, in the case of the first analysis, we suggested that
S exists both at contexts kant and bolzano. In this view, S is not new, the scholar histor-
ically retraces it according to her examination of the definitions provided by Kant and
Bolzano. The second analysis is more flexible because the generalization links between
S and SK /SB are acontextual: SK v S and SB v S may hold even though SK exists only at
the context kant, SB exists only at the context brentano, and S exists only at the context
betti vanderberg (i.e., S has been ‘created’ by Betti and van der Berg). This situation
is not precluded by the first analysis, but in this case S would not change from Kant to
Bolzano, actually it would not exist at these contexts. Vice versa, in the second analysis
one could also pretend that the stable concept S generalizing both SK and SB exists at the
contexts kant and brentano.
In general, by starting from stable concepts, historians may adopt some rules to de-
termine continuities among them—to determine the degrees of similarity between their
characterizations—and consequently introduce static or dynamic concepts that general-
ize or group together the concepts that result similar. For instance, starting from φK and
φB one may individuate a set of shared features or, by using the taxonomy of features,
to find an indirect link through a common generalization. Historians could then experi-
ment different rules and study their impact in terms of individuating possible continuities
among the original concepts.
However, the analysis of the previous example suggests that the commitment to-
wards dynamic concepts does not provide any analytical advantage to historians. Actu-
ally it seems to be less flexible for representing the temporal or contextual extensions
8 Structural similarities between characterizations allow to individuate more abstract links, see Sect. 5.4.
of concepts. From this perspective, our claim is that the introduction of new dynamic or
static concepts can be avoided without the loss of any analytical power. The continuity
between the original (stable) concepts can be captured by means of clusters of character-
izations individuated by means of the selected similarity relation R (usually an equiva-
lence relation). Clusters result explicitly determined by scholars through the adoption of
precise rules. However, scholars do not have to necessarily commit to the existence of a
dynamic or static concept collecting or generalizing all the characterizations in a given
cluster. In this perspective, historians can consider different relations R among concepts,
explore the effects of these choices in terms of the obtained clusters, and possibly in-
troduce concepts that correspond or generalize the characterizations in the clusters. The
discussed mechanism facilitates and makes explicit the analytical process under the de-
cisions of historians without forcing a strong commitment on the nature and the persis-
tence of concepts. The existence of static or dynamic concepts and their reduction to
given kinds of clusters concern the metaphysical realm rather than the historical one.
5.4. Structural Approaches
The previous sections analyze the characterization of concepts in terms of (possibly vary-
ing) features. However, as noted in [1], concepts are often part of a network of interlinked
concepts, which contributes to relationally characterize concepts themselves. We explore
in this section how to formally represent networks to augment concepts characterization.
Let us assume to represent a network of concepts by a relational structure
hC , R1 , . . . , Rn i where C is a set of concepts and Ri s are relations defined on C . Similarly
to concepts, networks are in time and may therefore undergo some changes. Our analysis
is however restricted to stable networks where Ri s are not temporally qualified and the
time(s) at which the network exists is indicated by a subscript, e.g., hC , R1 , . . . , Rn it ex-
ists at t and it characterizes all the concepts in C at t. In this sense, one could rethink the
γ relation to associate, at t, to each concept in C , the relational structure hC , R1 , . . . , Rn it ,
i.e., all the concepts in C are simultaneously characterized by the same network.
This approach is more powerful than the one purely based on features. For in-
stance, S1 = hC1 , R1 it1 with C1 = {Red, Blue, Ferrari} and the characterization relation
R1 = {hRed, Ferrarii} captures the situation where γ(Ferrari,t1 ) = {Red}. Similarly,
S2 = hC2 , R2 it2 with C2 = C1 and R2 = {hBlue, Ferrarii} capture the situation where
γ(Ferrari,t2 ) = {Blue}. However, S1 (S2 ) characterizes also Red (Blue) to be a feature
of Ferrari at t1 (t2 ). The tracking of concepts from S1 to S2 is here obtained by the
identity mapping µ : C1 → C2 and by the correspondence between R1 and R2 . However
additional relations can be introduced among the Ri s and different kinds of mappings can
be established (see Sect. 6).
Note that in this view the Ri s apply to concepts rather than to the individuals that
concepts classify. Differently, the notion of model considered in [1] refers to schemata
(i.e., a sort of conceptual graphs) where the relations apply to individuals. For instance,
a model where Finger and Hand are linked by the relation Part constrains the instances
of Finger to be part of the instances of Hand9 , a situation different from the previous
one where the feature Red is part of the concept Ferrari (that, in conceptual modeling
languages, is usually represented by means of attributes of concepts). Betti and van den
9 This is one of the possible semantics for this model. Different conceptual modeling languages have different
semantics and some of them, e.g., UML, do not have a clear semantics.
Berg argue that by using models it is possible to study the “history of concepts that do not
share any features in Kukkanen’s sense, but which occupy a similar place in the network
of concepts specified in a certain model” [1, p.829]. In order to understand what ‘similar
place’ means, consider a second model where Palm and Hand are linked by the relation
Part. Intuitively, palms and fingers have something in common, i.e., they are both part
of hands, they are both instances of the relational concept of Being part of an hand. A
third abstract model can be introduced with a general concept Being part of an hand
linked by the parthood relation to Hand. In this model, Being part of an hand represents
a ‘place’ that abstracts from the different kinds of parts of hands. We are here in presence
of a mechanism similar to the one analyzed in the case of taxonomically organized fea-
tures, however Finger and Palm are here generalized in the context of the relation their
instances have with the instances of Hand. It should be clear that this comparison does
not allow per se to understand historical links between concepts. However, the character-
ization of concepts by means of models allows for more abstract clustering mechanisms;
hence, by relying on the mappings between concepts and relations in different networks,
this approach captures abstract structural similarities among concepts.
6. The Intentional Dimension of Concepts
Up to now, the similarity relations grounding the construction of clusters rely on the fea-
tures of concepts and/or on the relations the concepts have with the other concepts in the
networks. The similarity between concepts reduces therefore to the similarity between
their features and/or the places they occupy in the conceptual networks.
Part of the analysis of historians is however also driven by explicit or implicit refer-
ence done by concepts’ creators to pre-existing concepts [2]. Scholars are indeed often
interested in historical evidence about the background or the influential sources of an
author and, especially, in the intentional dimension at the basis of the development of
a concept. Concepts’ authors may not introduce concepts from scratch and may rely on
existing concepts in the light of their aims. In this perspective, a certain author could in-
tend to specialize, generalize, modify, radically transform or discard a pre-existent con-
cept to achieve her goals.10 These links—studied and traced by historians—can be seen
as integral information about the intentional process of developing concepts, and finally
about concepts themselves.
The intentional links can be modeled by introducing a set ∆ of diachronic relations
between concepts. Each relation δ ∈ ∆ with form δ (Ci ,t,C j ,t 0 ) intuitively models the
fact that the concept Ci , as it is at time or context t, is intentionally δ -linked to the pre-
existing concept C j , as it is at time or context t 0 . For instance, Brentano could explicitly
refer to the notion of science of Kant with the intention of introducing a totally different
notion. Or, the historian could retrace that Brentano known the work of Kant and that he
started from this work to develop his own concept of science. First note that each δ ∈ ∆ is
a relation between concepts, not between their instances. Thus, as observed in Sect. 5.4,
these relations are not part of models as intended in [1]. Second, by linking concepts that
exist at different times or contexts these relations cannot be part of any hC , R1 , . . . , Rn it
structure where the Ri s are always synchronic (even when they apply to concepts). Thus,
10 Think about the classical debates in philosophy, e.g., on the concept of universal where a philosopher
reinterprets what done by previous philosophers in order to propose a new philosophical theory.
the relations in ∆ capture intentional dependencies that complement the characterization
of concepts based on features and models with a historical perspective.
∆-relations may be exploited to refine the similarity relations R and to individuate
(dis-)continuities in the history of concepts in terms of the intentions of creators. There
may be also cases in which authors could explicitly claim conceptual innovations or
transformations which are not directly reflected in the created concepts, i.e., their inten-
tions can be decoupled from the way in which concepts are characterized in terms of
features and models. The act of claiming some intentions, expressed via the ∆-relations,
about a concept Ci (at t) with respect to a pre-existing concept C j (at t 0 ) does not automat-
ically translate into a relation between the features or the models in γ(Ci ,t) and γ(C j ,t 0 ).
They rather make explicit the intentions of the authors that may be only marginally trans-
lated into the produced characterization γ(Ci ,t). The intentions of authors may then play
a central role for understanding the created concepts and for studying their behavior
through time, a crucial aspect in a historical perspective.
In general, one could think to represent the intentional dimension of concepts by
means of features. Rather than δ (Ci ,t,C j ,t 0 ) one could introduce in γ(Ci ,t) the feature Fδ
such that Fδ (C,t) if and only if δ (C,t,C j ,t 0 ). This kind of features is taken into account
neither by Kuukkanen [8] nor by Betti and van den Berg [1] and, as discussed in Sect. 5.2,
they would be properties of concepts (rather than properties of their instances). Note that,
using a similar strategy, the ‘places’ in the networks of concepts could also be reduced
to features. A deeper analysis on the ontological nature of features and on the possibility
to attribute them an exclusive role in the characterization of concepts is here required.
An alternative, and maybe more interesting, possibility consists in introducing the ∆-
relations as modal mappings between the concepts in the models that characterize the
concepts Ci and C j . The general idea is that the concepts in a model could be mapped
to the concepts in another model in different ways, according to different modalities and
intentionalities that can then be exploited by in the similarity relations R. Again, further
work on a deeper analysis of the nature of these intentional relations and on the way they
can be used is required.
7. Conclusions
We discussed in the paper some theoretical issues concerning the modeling of concepts
and concept drift for historical research in the context of the Digital Humanities. As
said in Sect. 1, this analysis is a first step for a robust modeling approach leading to an
ontology-based information system for the organization of historical data.
To sum up the results of our study, the phenomenon of concept drift, understood
as the possibility for a concept to change while keeping its identity, remains an open
topic for theoretical research. The challenge is to define concepts’ persistence conditions,
which is not a simple task given the abstract (and perhaps even vague) nature of concepts.
Our idea is that, in a historical perspective, this debate is secondary. We proposed to
first study the concepts introduced by the authors by considering the way in which they
are characterized in terms of features and/or models. Second, to introduce similarity
relations R among characterizations (and consequently among concepts) that can be used
to indivuate clusters of characterizations (and concepts). The study of these clusters and
the possibility of experiment alternative similarity relations generating different clusters
offer to historians a powerful analytical tool that is uncommitted with respect to the
fact that these clusters correspond or not to (new or already existing) concepts. In this
approach, one is not committed towards a specific stance in concept drift while being
able at the same time to investigate possible continuities or discontinuities among various
concepts.
Finally, differently from the state of the art, we proposed to take into account the
creation process from which concepts originate by considering, in particular, the authors’
intentional attitude towards pre-existing concepts from which the new concepts derive.
This allows to make sense of the fact that a concept is created by ‘manipulating’ some
pre-existing concepts. In this view, the building process of a concept, the way it originates
from pre-existing concepts, becomes part of the characterization of the concept itself.
Further work is necessary at both the theoretical and implementation level. From the
former perspective, as said, a deeper understanding of the nature of features and of con-
cepts’ characterization is due. In particular, it may be interesting to understand the kind
of relations holding between concepts when some derive from the others. From the latter
perspective, concepts’ characterization (in terms of the function γ) can be useful to semi-
automatically organize or cluster concepts. This may lead to an algorithmic procedure to
aid decision making for concepts organization in historical research.
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